Introduction:
FastAPI is a modern web framework for building APIs in Python, known for its speed and ease of use. In this article, you will learn how to implement a machine learning model and expose it as an API using FastAPI.
Key Sections:
Setting Up FastAPI: This section provides a brief introduction to FastAPI and its advantages for ML-based APIs. It also shows how to install and set up FastAPI.
Code Example: Basic FastAPI Application
from fastapi import FastAPI
app = FastAPI()
@app("/")
def read_root():
return {"Hello": "World"}
Training a Machine Learning Model: This section explains how to use a simple machine learning model (e.g., a scikit-learn regression model) and describes the training process.
Code Example: Training an ML Model
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model = LinearRegression().fit(X, y)
Exposing the Model as an API: This section demonstrates how to use FastAPI to create an endpoint that accepts input data and returns predictions.
Code Example: API Endpoint for Model Prediction
from pydantic import BaseModel
class PredictionInput(BaseModel):
x1: float
x2: float
@app("/predict")
def predict(input: PredictionInput):
prediction = model.predict([[input.x1, input.x2]])
return {"prediction": prediction[0]}
Conclusion: This section recaps how easy it is to integrate machine learning models into web services using FastAPI.Introduction:
FastAPI is a cutting-edge web framework designed for creating APIs in Python, recognized for its exceptional speed and user-friendly nature. In this guide, we will delve into the process of implementing a machine learning model and exposing it as an API using FastAPI.
Key Sections:
Setting Up FastAPI: This section offers an overview of FastAPI and its benefits for ML-based APIs, while also providing a step-by-step guide on installing and configuring FastAPI.
Code Example: Basic FastAPI Application
from fastapi import FastAPI
app = FastAPI()
@username_3("/")
def read_root():
return {"Hello": "World"}
Training a Machine Learning Model: Here, we will explore the process of utilizing a simple machine learning model, such as a scikit-learn regression model, and explain the training process in detail.
Code Example: Training an ML Model
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model = LinearRegression().fit(X, y)
Exposing the Model as an API: This section will illustrate how to utilize FastAPI to create an endpoint that can accept input data and provide predictions in return.
Code Example: API Endpoint for Model Prediction
from pydantic import BaseModel
class PredictionInput(BaseModel):
x1: float
x2: float
@username_3("/predict")
def predict(input: PredictionInput):
prediction = model.predict([[input.x1, input.x2]])
return {"prediction": prediction[0]}
Conclusion: In this section, we will summarize the seamless process of integrating machine learning models into web services using FastAPI.